{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification by Wine Type, Part 2\n",
    "\n",
    "## Wine Data\n",
    "Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "\n",
    "### Citations\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "Available at:\n",
    "- [@Elsevier](http://dx.doi.org/10.1016/j.dss.2009.05.016)\n",
    "- [Pre-press (pdf)](http://www3.dsi.uminho.pt/pcortez/winequality09.pdf)\n",
    "- [bib](http://www3.dsi.uminho.pt/pcortez/dss09.bib)\n",
    "\n",
    "Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_wine = pd.read_csv('data/winequality-red.csv')\n",
    "white_wine = pd.read_csv('data/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we completed our EDA in the [wine.ipynb notebook for last chapter](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/blob/master/ch_09/wine.ipynb), we will just look at some rows to refresh our memory of the data rather than repeating the EDA here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.081</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.99689</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.13</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.044</td>\n",
       "      <td>26.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.99772</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.049</td>\n",
       "      <td>17.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99370</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.075</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.00005</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.84</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.122</td>\n",
       "      <td>45.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.99360</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.31</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "848             6.4              0.64         0.21             1.8      0.081   \n",
       "2529            6.6              0.42         0.13            12.8      0.044   \n",
       "131             5.6              0.50         0.09             2.3      0.049   \n",
       "244            15.0              0.21         0.44             2.2      0.075   \n",
       "1551            6.6              0.19         0.99             1.2      0.122   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "848                  14.0                  31.0  0.99689  3.59       0.66   \n",
       "2529                 26.0                 158.0  0.99772  3.24       0.47   \n",
       "131                  17.0                  99.0  0.99370  3.63       0.63   \n",
       "244                  10.0                  24.0  1.00005  3.07       0.84   \n",
       "1551                 45.0                 129.0  0.99360  3.09       0.31   \n",
       "\n",
       "      alcohol  quality   kind  \n",
       "848       9.8        5    red  \n",
       "2529      9.0        5  white  \n",
       "131      13.0        5    red  \n",
       "244       9.2        7    red  \n",
       "1551      8.7        6  white  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.concat([\n",
    "    white_wine.assign(kind='white'), red_wine.assign(kind='red')\n",
    "])\n",
    "wine.sample(5, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "wine_y = np.where(wine.kind == 'red', 1, 0)\n",
    "wine_X = wine.drop(columns=['quality', 'kind'])\n",
    "\n",
    "w_X_train, w_X_test, w_y_train, w_y_test = train_test_split(\n",
    "    wine_X, wine_y, test_size=0.25, random_state=0, stratify=wine_y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Classification of Red and White Wines from Chapter 9\n",
    "This was the result from chapter 9 for reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "white_or_red = Pipeline([\n",
    "    ('scale', StandardScaler()), ('lr', LogisticRegression(solver='lbfgs', random_state=0))\n",
    "]).fit(w_X_train, w_y_train)\n",
    "\n",
    "kind_preds = white_or_red.predict(w_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model performed very well without any tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1225\n",
      "           1       0.99      0.98      0.98       400\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1625\n",
      "   macro avg       0.99      0.99      0.99      1625\n",
      "weighted avg       0.99      0.99      0.99      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(w_y_test, kind_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116ac7f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_roc\n",
    "\n",
    "plot_roc(w_y_test, white_or_red.predict_proba(w_X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1275b070>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(w_y_test, kind_preds, ['white', 'red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction\n",
    "### Variance Threshold\n",
    "Features with little to no variance don't contribute much to our classification model. We can remove those and work with the features that have some variance. By default, the `VarianceThreshold` class will remove all features that have the same value throughout the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "white_or_red_min_var = Pipeline([\n",
    "    ('feature_selection', VarianceThreshold(0.01)), # keep features with variance > 0.01\n",
    "    ('scale', StandardScaler()), \n",
    "    ('lr', LogisticRegression(solver='lbfgs', random_state=0))\n",
    "]).fit(w_X_train, w_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check which features got removed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['chlorides', 'density'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_X_train.columns[\n",
    "    ~white_or_red_min_var.named_steps[\n",
    "        'feature_selection'\n",
    "    ].get_support()\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance doesn't change much:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99      1225\n",
      "           1       0.98      0.95      0.96       400\n",
      "\n",
      "   micro avg       0.98      0.98      0.98      1625\n",
      "   macro avg       0.98      0.97      0.97      1625\n",
      "weighted avg       0.98      0.98      0.98      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(\n",
    "    w_y_test, white_or_red_min_var.predict(w_X_test)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Components Analysis\n",
    "Can we see a way to easily separate these that might help us?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wine Kind PCA (2 components)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.pca import pca_scatter\n",
    "pca_scatter(wine_X, wine_y, 'wine is red?')\n",
    "plt.title('Wine Kind PCA (2 components)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Wine Type PCA (3 components)')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.pca import pca_scatter_3d\n",
    "pca_scatter_3d(wine_X, wine_y, 'wine is red?', elev=20, azim=-10)\n",
    "plt.suptitle('Wine Type PCA (3 components)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many PCA components explain most of the variance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12aea130>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from ml_utils.pca import pca_explained_variance_plot\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('normalize', MinMaxScaler()), ('pca', PCA(8, random_state=0))\n",
    "]).fit(w_X_train, w_y_train) \n",
    "\n",
    "pca_explained_variance_plot(pipeline.named_steps['pca'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a scree plot to find the number of components to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12afec90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from ml_utils.pca import pca_scree_plot\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('normalize', MinMaxScaler()), ('pca', PCA(8, random_state=0))\n",
    "]).fit(w_X_train, w_y_train)\n",
    "\n",
    "pca_scree_plot(pipeline.named_steps['pca'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Will a model fit on these components perform better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.69231352, 19.27678507,  2.2161272 , -4.0772633 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('normalize', MinMaxScaler()),\n",
    "    ('pca', PCA(4, random_state=0)),\n",
    "    ('lr', LogisticRegression(\n",
    "        solver='lbfgs', class_weight='balanced', random_state=0\n",
    "    ))\n",
    "]).fit(w_X_train, w_y_train)\n",
    "\n",
    "pipeline.named_steps['lr'].coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the agreement between our new model and the original:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9633975284373049"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agreement with logistic regression alone\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "\n",
    "cohen_kappa_score(kind_preds, pipeline.predict(w_X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance is still good using dimensionality reduction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1225\n",
      "           1       0.96      0.96      0.96       400\n",
      "\n",
      "   micro avg       0.98      0.98      0.98      1625\n",
      "   macro avg       0.98      0.98      0.98      1625\n",
      "weighted avg       0.98      0.98      0.98      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "preds = pipeline.predict(w_X_test)\n",
    "print(classification_report(w_y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ac51b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_roc\n",
    "plot_roc(w_y_test, pipeline.predict_proba(w_X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b853f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "confusion_matrix_visual(w_y_test, preds, ['low', 'high'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can a decision tree tell us what features are important?\n",
    "Decision trees give us feature importances. These sum up to 1 with the highest being more important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>feature</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>density</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>pH</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>importance</th>\n",
       "      <td>0.687236</td>\n",
       "      <td>0.210241</td>\n",
       "      <td>0.050201</td>\n",
       "      <td>0.016196</td>\n",
       "      <td>0.012143</td>\n",
       "      <td>0.01143</td>\n",
       "      <td>0.005513</td>\n",
       "      <td>0.005074</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "feature     total sulfur dioxide  chlorides   density  volatile acidity  \\\n",
       "importance              0.687236   0.210241  0.050201          0.016196   \n",
       "\n",
       "feature     sulphates       pH  residual sugar   alcohol  fixed acidity  \\\n",
       "importance   0.012143  0.01143        0.005513  0.005074       0.001811   \n",
       "\n",
       "feature     citric acid  free sulfur dioxide  \n",
       "importance     0.000113             0.000042  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier(random_state=0).fit(w_X_train, w_y_train)\n",
    "pd.DataFrame([(col, coef) for col, coef in zip(\n",
    "    w_X_train.columns, dt.feature_importances_\n",
    ")], columns=['feature', 'importance']\n",
    ").set_index('feature').sort_values(\n",
    "    'importance', ascending=False\n",
    ").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the decisions the tree made:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<text text-anchor=\"middle\" x=\"246\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">total sulfur dioxide &lt;= 66.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"246\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.371</text>\r\n",
       "<text text-anchor=\"middle\" x=\"246\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4872</text>\r\n",
       "<text text-anchor=\"middle\" x=\"246\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3673, 1199]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"175\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.173</text>\r\n",
       "<text text-anchor=\"middle\" x=\"175\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1044</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"317\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">chlorides &lt;= 0.069</text>\r\n",
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       "<!-- 4&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x12baecb0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "\n",
    "graphviz.Source(export_graphviz(\n",
    "    DecisionTreeClassifier(\n",
    "        max_depth=2, random_state=0\n",
    "    ).fit(w_X_train, w_y_train),\n",
    "    feature_names=w_X_train.columns\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the top 2 features\n",
    "Looking at the top 2 features, we can see if its possible to separate red and white wine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=wine['total sulfur dioxide'], y=wine['chlorides'], hue=wine.kind, alpha=0.25)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Top 2 Features for Classification of Red and White Wines\n",
    "We can build a model with just these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "important_features = ['total sulfur dioxide', 'chlorides']\n",
    "X_train = w_X_train[important_features]\n",
    "X_test = w_X_test[important_features]\n",
    "\n",
    "white_or_red_top_features = Pipeline([\n",
    "    ('scale', StandardScaler()), ('lr', LogisticRegression(solver='lbfgs', random_state=0))\n",
    "]).fit(X_train, w_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notice our performance is still good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_features_kind_preds = white_or_red_top_features.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.98      0.97      1225\n",
      "           1       0.94      0.85      0.90       400\n",
      "\n",
      "   micro avg       0.95      0.95      0.95      1625\n",
      "   macro avg       0.95      0.92      0.93      1625\n",
      "weighted avg       0.95      0.95      0.95      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(w_y_test, top_features_kind_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12e14770>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_roc\n",
    "\n",
    "plot_roc(w_y_test, white_or_red_top_features.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12dd2550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(w_y_test, top_features_kind_preds, ['white', 'red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Analysis on Logistic Regression\n",
    "We can look at the incorrect predictions to get a better feel for our data and the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_probabilities = pd.DataFrame(\n",
    "    white_or_red.predict_proba(w_X_test), \n",
    "    columns=['prob_white', 'prob_red']\n",
    ").assign(\n",
    "    is_red=w_y_test == 1,\n",
    "    pred_white=lambda x: x.prob_white >= 0.5, \n",
    "    pred_red=lambda x: np.invert(x.pred_white),\n",
    "    correct=lambda x: (np.invert(x.is_red) & x.pred_white)\\\n",
    "    | (x.is_red & x.pred_red)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prob_white</th>\n",
       "      <th>prob_red</th>\n",
       "      <th>is_red</th>\n",
       "      <th>pred_white</th>\n",
       "      <th>pred_red</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.994654</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.999605</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.999674</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>0.999278</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.999683</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     prob_white  prob_red  is_red  pred_white  pred_red  correct\n",
       "443    0.994654  0.005346   False        True     False     True\n",
       "319    0.999605  0.000395   False        True     False     True\n",
       "703    0.000326  0.999674    True       False      True     True\n",
       "526    0.999278  0.000722   False        True     False     True\n",
       "76     0.999683  0.000317   False        True     False     True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_probabilities.sample(5, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of prediction confidence\n",
    "When our model is confident, it is usually right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Prediction Confidence')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "for ax, state, color in zip(\n",
    "    axes, ['correct', 'not correct'], ['purple', 'orange']\n",
    "):\n",
    "    sns.distplot(\n",
    "        prediction_probabilities.query(state).prob_red,\n",
    "        ax=ax, rug=True, bins=20, color=color\n",
    "    )\n",
    "    ax.set_xlabel('probability wine is red')\n",
    "    ax.set_ylabel('density')\n",
    "    ax.set_title(f'prediction was {state}')\n",
    "plt.suptitle('Prediction Confidence')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are the incorrect classifications outliers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"Comparing Chemical Properties of Red and White Wines\\n(classification errors are red x's)\")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "incorrect = w_X_test.assign(is_red=w_y_test).iloc[prediction_probabilities.query('not correct').index,:]\n",
    "chemical_properties = [col for col in wine.columns if col not in ['quality', 'kind']]\n",
    "melted = wine.drop(columns='quality').melt(id_vars=['kind'])\n",
    "\n",
    "fig, axes = plt.subplots(math.ceil(len(chemical_properties) / 4), 4, figsize=(20, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for prop, ax in zip(chemical_properties, axes):\n",
    "    sns.boxplot(\n",
    "        data=melted[melted.variable.isin([prop])], \n",
    "        x='variable', y='value', hue='kind', ax=ax,\n",
    "        palette='RdBu_r', saturation=0.5, fliersize=2\n",
    "    )\n",
    "    for _, wrong in incorrect.iterrows():\n",
    "        x_coord = -0.2 if not wrong['is_red'] else 0.2\n",
    "        ax.scatter(x_coord, wrong[prop], marker='x', color='red', s=50)\n",
    "    \n",
    "# remove the extra subplots\n",
    "for ax in axes[len(chemical_properties):]:\n",
    "    ax.remove()\n",
    "    \n",
    "plt.suptitle(\n",
    "    'Comparing Chemical Properties of Red and White Wines'\n",
    "    '\\n(classification errors are red x\\'s)'\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
